Dynamic Spatial-temporal Hypergraph Convolutional Network for Skeleton-based Action Recognition
This work addresses the problem of improving action recognition accuracy for applications like surveillance or human-computer interaction, but it is incremental as it builds on existing hypergraph methods by adding time-point dependencies.
The paper tackles skeleton-based action recognition by proposing a dynamic spatial-temporal hypergraph convolutional network (DST-HCN) to capture spatial-temporal information, achieving state-of-the-art results on datasets like NTU RGB+D and NW-UCLA.
Skeleton-based action recognition relies on the extraction of spatial-temporal topological information. Hypergraphs can establish prior unnatural dependencies for the skeleton. However, the existing methods only focus on the construction of spatial topology and ignore the time-point dependence. This paper proposes a dynamic spatial-temporal hypergraph convolutional network (DST-HCN) to capture spatial-temporal information for skeleton-based action recognition. DST-HCN introduces a time-point hypergraph (TPH) to learn relationships at time points. With multiple spatial static hypergraphs and dynamic TPH, our network can learn more complete spatial-temporal features. In addition, we use the high-order information fusion module (HIF) to fuse spatial-temporal information synchronously. Extensive experiments on NTU RGB+D, NTU RGB+D 120, and NW-UCLA datasets show that our model achieves state-of-the-art, especially compared with hypergraph methods.